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Stata Panel Data

You cannot estimate the coefficients of variables that do not change over time (e.g., race, gender, or country of origin), as they are dropped during the transformation. Random Effects (RE) Model

Variation over time within the same entities (ignoring differences between entities). Visualizing Panel Trajectories

: Run xtreg, fe vce(cluster id) as default. Always. Even if you think errors are i.i.d.—they aren’t.

Visual and descriptive exploration helps you understand the variation within your dataset before moving to complex statistical modeling. Summary Statistics stata panel data

Alternative to RE. Does not model the individual effect explicitly but accounts for the correlation structure within panels.

In almost all real-world microeconomic panel datasets, observations within the same entity are correlated over time. Failing to account for this will deflate your standard errors and artificially inflate your

isid idcode year, sort duplicates report idcode year duplicates drop idcode year, force // Use with caution You cannot estimate the coefficients of variables that

Each row represents an entity, with separate columns for each time period (e.g., income2020 , income2021 ).

Before analysis, you must declare the data to be panel data.

Choosing blindly between Pooled OLS, FE, and RE can invalidate your empirical findings. Stata provides specific post-estimation tests to guide your selection. Always

For macroeconomic applications where multiple panel variables endogenously influence one another over time, you can implement a Panel VAR model using the pvar suite: pvar income consumption investment, lags(2) Use code with caution. Summary Checklist for Stata Panel Analysis

If your data is in a wide format, convert it using the reshape command: reshape long income, i(id) j(year) Use code with caution. Setting the Panel Structure

eststo clear eststo: reg ln_wage hours age tenure, vce(cluster idcode) eststo: xtreg ln_wage hours age tenure, fe eststo: xtreg ln_wage hours age tenure, re esttab est1 est2 est3, se star(* 0.10 ** 0.05 *** 0.01) /// mtitles("Pooled OLS" "Fixed Effects" "Random Effects") /// addnotes("Standard errors clustered at individual level")

). If your data is in a "wide" format (e.g., separate columns for income in 2020, 2021, and 2022), you must reshape it first. Reshaping Data

pwcorr wage hours tenure age